kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "kaggle泰坦尼克之灾"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "一.导入数据包与数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train=pd.read_csv(r'H:\\kaggle\\train.csv')\n",
    "test=pd.read_csv(r'H:\\kaggle\\test.csv')\n",
    "PassengerId=test['PassengerId']\n",
    "all_data = pd.concat([train, test], ignore_index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "二.数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.总体预览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "•PassengerID（ID） \n",
    "•Survived(存活与否) \n",
    "•Pclass（客舱等级，较为重要） \n",
    "•Name（姓名，可提取出更多信息） \n",
    "•Sex（性别，较为重要） \n",
    "•Age（年龄，较为重要） \n",
    "•Parch（直系亲友） \n",
    "•SibSp（旁系） \n",
    "•Ticket（票编号） \n",
    "•Fare（票价） \n",
    "•Cabin（客舱编号） \n",
    "•Embarked（上船的港口编号）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      "PassengerId    891 non-null int64\n",
      "Survived       891 non-null int64\n",
      "Pclass         891 non-null int64\n",
      "Name           891 non-null object\n",
      "Sex            891 non-null object\n",
      "Age            714 non-null float64\n",
      "SibSp          891 non-null int64\n",
      "Parch          891 non-null int64\n",
      "Ticket         891 non-null object\n",
      "Fare           891 non-null float64\n",
      "Cabin          204 non-null object\n",
      "Embarked       889 non-null object\n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.6+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2..数据初步分析,使用统计学与绘图\n",
    "目的:初步了解数据之间的相关性,为构造特征工程以及模型建立做准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1)Sex Feature：女性幸存率远高于男性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb120ef0>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+Ys1rR12CpHOMw0eSpIahIElqGAqSpIahIElqGAqSpIahIElqGAqSpIahIElqtBoKSdYn\nOZhkKsnWAftfmeRPkswmeX2btUiS5tdaKCRZAuwErgbWApuSrO3r9g3geuBjbdUhSRpem9NcrAOm\nquoQQJLdwAbgwIkOVfVAd99jLdYhSRpSm8NHy4HDPe3p7jZJ0jmqzVDIgG31uA6UbE4ymWRyZmbm\nDMuSJJ1Km6EwDazsaa8Ajj6eA1XVrqoar6rxsbGxBSlOknSyNkNhH7AmyeokS4GNwESLrydJOkOt\nhUJVzQJbgL3AfcBtVbU/yfYk1wAkeVmSaeANwAeT7G+rHknS/FpdZKeq9gB7+rZt63m8j86wkiTp\nHOAdzZKkhqEgSWoYCpKkhqEgSWoYCpKkhqEgSWoYCpKkhqEgSWoYCpKkhqEgSWoYCpKkhqEgSWoY\nCpKkhqEgSWoYCpKkhqEgSWoYCpKkRquhkGR9koNJppJsHbD/R5J8orv/S0lWtVmPJGlurYVCkiXA\nTuBqYC2wKcnavm43AN+uqucB7wP+fVv1SJLm1+aZwjpgqqoOVdVxYDewoa/PBuAj3ce3A69OkhZr\nkiTNoc1QWA4c7mlPd7cN7FNVs8B3gWe2WJMkaQ4XtnjsQb/x1+PoQ5LNwOZu8/tJDp5hbfpry4AH\nR13EuSD/4Y2jLkE/zPfmCf9mQQZQnjNMpzZDYRpY2dNeARw9RZ/pJBcCTwMe6j9QVe0CdrVU53kt\nyWRVjY+6Dqmf783RaHP4aB+wJsnqJEuBjcBEX58J4MSvZ68HPlNVJ50pSJLOjtbOFKpqNskWYC+w\nBLi1qvYn2Q5MVtUE8J+BjyaZonOGsLGteiRJ84u/mJ/fkmzuDs9J5xTfm6NhKEiSGk5zIUlqGApq\nJLkyye+Nug4tDkluSnJfkt9u6fjvTHJzG8c+n7X5kVRJ57d/DlxdVfePuhANzzOFRSbJqiRfS/Jb\nSb6a5LeTXJXkziR/lmRd9+vzSb7c/fMFA45zcZJbk+zr9uufokQ6pSS/CfxtYCLJ2we9l5Jcn+R3\nk3wqyf1JtiR5S7fPF5M8o9vvF7vP/UqSTyZ58oDXe26S/5XkriR/lOSFZ/c7XjwMhcXpecCvAZcB\nLwSuBX4CuBn4V8DXgFdW1eXANuDfDTjG2+ncN/Iy4FXAe5NcfBZq1yJQVW+ic7Pqq4CLOfV76UV0\n3p/rgHcDf9l9X34B+IVun/9WVS+rqpcA99GZSLPfLuDGqrqCzvv8A+18Z4ufw0eL0/1VdS9Akv3A\np6uqktwLrKJz5/hHkqyhM63IkwYc47XANT1jthcBz6bzn1I6Had6LwHcUVUPAw8n+S7wqe72e+n8\nUgPwoiS/AjwdeAqde58aSZ4C/D3gd3rm0/yRNr6R84GhsDg90vP4sZ72Y3T+zd9F5z/jP+6uYfHZ\nAccI8Lqqcp4pnamB76UkL2f+9yrAh4GfqaqvJLkeuLLv+BcA36mqly5s2ecnh4/OT08DjnQfX3+K\nPnuBG09MZZ7k8rNQlxanM30vXQJ8M8mTgOv6d1bV94D7k7yhe/wkeckZ1nzeMhTOTzuA9yS5k84U\nJIO8i86w0j1JvtptS4/Hmb6X/jXwJeAP6FwPG+Q64IYkXwH2c/LaLRqSdzRLkhqeKUiSGoaCJKlh\nKEiSGoaCJKlhKEiSGoaCdBq68/jsT3JPkru7N2BJi4Z3NEtDSvIK4B8Bf6eqHkmyDFg64rKkBeWZ\ngjS8ZwEPVtUjAFX1YFUdTXJFks91Z+jcm+RZSS7szux5JUCS9yR59yiLl4bhzWvSkLoTr/0x8GTg\nD4FPAJ8HPgdsqKqZJD8P/FRV/dMkPwbcDtxE5y7yl1fV8dFULw3H4SNpSFX1/SRXAH+fzhTQnwB+\nhc70z3/QndpnCfDNbv/9ST5KZ+bPVxgIeiIwFKTTUFWP0plV9rPdqcjfDOyvqlec4ikvBr4D/K2z\nU6F0ZrymIA0pyQu6a1Cc8FI660uMdS9Ck+RJ3WEjkvws8EzglcD7kzz9bNcsnS6vKUhD6g4d/Sc6\ni73MAlPAZmAF8H46U5JfCPxH4L/Tud7w6qo6nOQm4IqqeuMoapeGZShIkhoOH0mSGoaCJKlhKEiS\nGoaCJKlhKEiSGoaCJKlhKEiSGoaCJKnx/wEHl2avl18UMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0dceb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Sex\", y=\"Survived\", data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2)Pclass Feature：乘客社会等级越高，幸存率越高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb6755f8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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J8fHxeQknSXqyLkthH7A6yaokS4GNwO4OX0+S9BR1VgpVdRbYDOwFDgN3VNXBJNuSrAdI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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3b41198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Pclass\", y=\"Survived\", data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3)SibSp Feature：配偶及兄弟姐妹数适中的乘客幸存率更高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb6f3c18>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SYyhIkhpDQZLUGAqSpMZQkCQ1vYZCklVJtifZkeSqEa//YJJPdq9/KclZfdYjSTq03kIh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\nIElq/h/z3II83ReluQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb300828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"SibSp\", y=\"Survived\", data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4)Parch Feature：父母与子女数适中的乘客幸存率更高"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb761c18>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c7z3A0h80Tzb/0lsFPPBfJ7Cj935uef/ZP4TnjzOoz1KYAtbN2F4L3HOIMVNJVgLPAR6a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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x5772fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Parch\", y=\"Survived\", data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5)从不同生还情况的密度图可以看出，在年龄15岁的左侧，生还率有明显差别，密度图非交叉区域面积非常大，但在其他年龄段，则差别不是很明显，认为是随机所致，因此可以考虑将此年龄偏小的区域分离出来。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(12.3598,0.5,'density')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tt0VNnDix1GQyERgYaI2MjKx78803AwCsVisbNmzwABgxYkTF66+/Hgjw+uuv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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb70ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "facet = sns.FacetGrid(train, hue=\"Survived\",aspect=2)\n",
    "facet.map(sns.kdeplot,'Age',shade= True)\n",
    "facet.set(xlim=(0, train['Age'].max()))\n",
    "facet.add_legend()\n",
    "plt.xlabel('Age') \n",
    "plt.ylabel('density') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6)Embarked登港港口与生存情况的分析 结果分析:C地的生存率更高,这个也应该保留为模型特征."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb867eb8>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dd3FmZlY+pR6k/gGF5zUcS+ExoD/N2szM7ABVakBURcQPImJn9poP+HFuZmYHsFIDYouk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\negAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb714048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot('Embarked',hue='Survived',data=train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "7)Title Feature(New)：不同称呼的乘客幸存率不同"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "新增Title特征，从姓名中提取乘客的称呼，归纳为六类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_data['Title'] = all_data['Name'].apply(lambda x:x.split(',')[1].split('.')[0].strip())\n",
    "Title_Dict = {}\n",
    "Title_Dict.update(dict.fromkeys(['Capt', 'Col', 'Major', 'Dr', 'Rev'], 'Officer'))\n",
    "Title_Dict.update(dict.fromkeys(['Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty'))\n",
    "Title_Dict.update(dict.fromkeys(['Mme', 'Ms', 'Mrs'], 'Mrs'))\n",
    "Title_Dict.update(dict.fromkeys(['Mlle', 'Miss'], 'Miss'))\n",
    "Title_Dict.update(dict.fromkeys(['Mr'], 'Mr'))\n",
    "Title_Dict.update(dict.fromkeys(['Master','Jonkheer'], 'Master'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb8d87b8>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cN6nNccCnmsefA96YJB3WJEmaRpehsDdwf9/y2mbdlG2qagPwKLBnhzVJkqaRqupmw8kJ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\nNooGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb7709e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_data['Title'] = all_data['Title'].map(Title_Dict)\n",
    "sns.barplot(x=\"Title\", y=\"Survived\", data=all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "8)FamilyLabel Feature(New)：家庭人数为2到4的乘客幸存率较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "新增FamilyLabel特征，先计算FamilySize=Parch+SibSp+1，然后把FamilySize分为三类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xb9ebd30>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XV1eHLpKkaXV2pFBVW5KcBVwFLAY+XVW3JTkf2FBV64BPAZ9JspHBEcIpXeWRJE2vy+Ej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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb6e9390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_data['FamilySize']=all_data['SibSp']+all_data['Parch']+1\n",
    "sns.barplot(x=\"FamilySize\", y=\"Survived\", data=all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按生存率把FamilySize分为三类，构成FamilyLabel特征。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xba03390>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xb8aa898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Fam_label(s):\n",
    "    if (s >= 2) & (s <= 4):\n",
    "        return 2\n",
    "    elif ((s > 4) & (s <= 7)) | (s == 1):\n",
    "        return 1\n",
    "    elif (s > 7):\n",
    "        return 0\n",
    "all_data['FamilyLabel']=all_data['FamilySize'].apply(Fam_label)\n",
    "sns.barplot(x=\"FamilyLabel\", y=\"Survived\", data=all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "9)Deck Feature(New)：不同甲板的乘客幸存率不同"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "新增Deck特征，先把Cabin空缺值填充为'Unknown'，再提取Cabin中的首字母构成乘客的甲板号。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbadc048>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GLk9y9oxmbwK+WVU/CrwX+K2u6pEkjddlT+E8YFdV3VdVTwA3ApfMaHMJ8OHh+48Br0iS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\nWNeAXreYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9fc160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_data['Cabin'] = all_data['Cabin'].fillna('Unknown')\n",
    "all_data['Deck']=all_data['Cabin'].str.get(0)\n",
    "sns.barplot(x=\"Deck\", y=\"Survived\", data=all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "10)TicketGroup Feature(New)：与2至4人共票号的乘客幸存率较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "新增TicketGroup特征，统计每个乘客的共票号数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xbd6e6a0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xbad62b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Ticket_Count = dict(all_data['Ticket'].value_counts())\n",
    "all_data['TicketGroup'] = all_data['Ticket'].apply(lambda x:Ticket_Count[x])\n",
    "sns.barplot(x='TicketGroup', y='Survived', data=all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "按生存率把TicketGroup分为三类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xc014f28>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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UnQ7cMeoi9JAce5/dr3liPuCY+/xyyWF9fo8dplOfoTAFrBporwRuP0SfqSQnAd8G3Dn7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\nkVNQi0EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbd60e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def Ticket_Label(s):\n",
    "    if (s >= 2) & (s <= 4):\n",
    "        return 2\n",
    "    elif ((s > 4) & (s <= 8)) | (s == 1):\n",
    "        return 1\n",
    "    elif (s > 8):\n",
    "        return 0\n",
    "\n",
    "all_data['TicketGroup'] = all_data['TicketGroup'].apply(Ticket_Label)\n",
    "sns.barplot(x='TicketGroup', y='Survived', data=all_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.数据清洗"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1)缺失值填充"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Age Feature：Age缺失量为263，缺失量较大，用Sex, Title, Pclass三个特征构建随机森林模型，填充年龄缺失值。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "age_df = all_data[['Age', 'Pclass','Sex','Title']]\n",
    "age_df=pd.get_dummies(age_df)\n",
    "known_age = age_df[age_df.Age.notnull()].as_matrix()\n",
    "unknown_age = age_df[age_df.Age.isnull()].as_matrix()\n",
    "y = known_age[:, 0]\n",
    "X = known_age[:, 1:]\n",
    "rfr = RandomForestRegressor(random_state=0, n_estimators=100, n_jobs=-1)\n",
    "rfr.fit(X, y)\n",
    "predictedAges = rfr.predict(unknown_age[:, 1::])\n",
    "all_data.loc[ (all_data.Age.isnull()), 'Age' ] = predictedAges "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Embarked Feature：Embarked缺失量为2，缺失Embarked信息的乘客的Pclass均为1，且Fare均为80，因为Embarked为C且Pclass为1的乘客的Fare中位数为80，所以缺失值填充为C。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>FamilyLabel</th>\n",
       "      <th>Deck</th>\n",
       "      <th>TicketGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>38.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Icard, Miss. Amelie</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113572</td>\n",
       "      <td>Miss</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>62.0</td>\n",
       "      <td>B28</td>\n",
       "      <td>NaN</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Stone, Mrs. George Nelson (Martha Evelyn)</td>\n",
       "      <td>0</td>\n",
       "      <td>830</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>113572</td>\n",
       "      <td>Mrs</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age Cabin Embarked  Fare                                       Name  \\\n",
       "61   38.0   B28      NaN  80.0                        Icard, Miss. Amelie   \n",
       "829  62.0   B28      NaN  80.0  Stone, Mrs. George Nelson (Martha Evelyn)   \n",
       "\n",
       "     Parch  PassengerId  Pclass     Sex  SibSp  Survived  Ticket Title  \\\n",
       "61       0           62       1  female      0       1.0  113572  Miss   \n",
       "829      0          830       1  female      0       1.0  113572   Mrs   \n",
       "\n",
       "     FamilySize  FamilyLabel Deck  TicketGroup  \n",
       "61            1            1    B            2  \n",
       "829           1            1    B            2  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data[all_data['Embarked'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pclass  Embarked\n",
       "1       C           78.2667\n",
       "        Q           90.0000\n",
       "        S           52.0000\n",
       "2       C           15.3146\n",
       "        Q           12.3500\n",
       "        S           15.3750\n",
       "3       C            7.8958\n",
       "        Q            7.7500\n",
       "        S            8.0500\n",
       "Name: Fare, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.groupby(by=[\"Pclass\",\"Embarked\"]).Fare.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_data['Embarked'] = all_data['Embarked'].fillna('C')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fare Feature：Fare缺失量为1，缺失Fare信息的乘客的Embarked为S，Pclass为3，所以用Embarked为S，Pclass为3的乘客的Fare中位数填充。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Name</th>\n",
       "      <th>Parch</th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Sex</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Title</th>\n",
       "      <th>FamilySize</th>\n",
       "      <th>FamilyLabel</th>\n",
       "      <th>Deck</th>\n",
       "      <th>TicketGroup</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1043</th>\n",
       "      <td>60.5</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>0</td>\n",
       "      <td>1044</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3701</td>\n",
       "      <td>Mr</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>U</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Age    Cabin Embarked  Fare                Name  Parch  PassengerId  \\\n",
       "1043  60.5  Unknown        S   NaN  Storey, Mr. Thomas      0         1044   \n",
       "\n",
       "      Pclass   Sex  SibSp  Survived Ticket Title  FamilySize  FamilyLabel  \\\n",
       "1043       3  male      0       NaN   3701    Mr           1            1   \n",
       "\n",
       "     Deck  TicketGroup  \n",
       "1043    U            1  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data[all_data['Fare'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "fare=all_data[(all_data['Embarked'] == \"S\") & (all_data['Pclass'] == 3)].Fare.median()\n",
    "all_data['Fare']=all_data['Fare'].fillna(fare)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2)同组识别"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把姓氏相同的乘客划分为同一组，从人数大于一的组中分别提取出每组的妇女儿童和成年男性。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_data['Surname']=all_data['Name'].apply(lambda x:x.split(',')[0].strip())\n",
    "Surname_Count = dict(all_data['Surname'].value_counts())\n",
    "all_data['FamilyGroup'] = all_data['Surname'].apply(lambda x:Surname_Count[x])\n",
    "Female_Child_Group=all_data.loc[(all_data['FamilyGroup']>=2) & ((all_data['Age']<=12) | (all_data['Sex']=='female'))]\n",
    "Male_Adult_Group=all_data.loc[(all_data['FamilyGroup']>=2) & (all_data['Age']>12) & (all_data['Sex']=='male')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现绝大部分女性和儿童组的平均存活率都为1或0，即同组的女性和儿童要么全部幸存，要么全部遇难。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GroupCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1.000000</th>\n",
       "      <td>115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.750000</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.333333</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.142857</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GroupCount\n",
       "1.000000         115\n",
       "0.000000          31\n",
       "0.750000           2\n",
       "0.333333           1\n",
       "0.142857           1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Female_Child=pd.DataFrame(Female_Child_Group.groupby('Surname')['Survived'].mean().value_counts())\n",
    "Female_Child.columns=['GroupCount']\n",
    "Female_Child"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'AverageSurvived')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CktTLgJAk9TIgJEm95oy7gKmSHAB8CFgP+FhVnTDmkqS10qVPf8a4S1jtnnHZpeMuYa0y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\n/w8GH/GjZDSqIQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc72d6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=Female_Child.index, y=Female_Child[\"GroupCount\"]).set_xlabel('AverageSurvived')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "绝大部分成年男性组的平均存活率也为1或0。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GroupCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000000</th>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.000000</th>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.500000</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.333333</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.250000</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          GroupCount\n",
       "0.000000         122\n",
       "1.000000          20\n",
       "0.500000           6\n",
       "0.333333           2\n",
       "0.250000           1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Male_Adult=pd.DataFrame(Male_Adult_Group.groupby('Surname')['Survived'].mean().value_counts())\n",
    "Male_Adult.columns=['GroupCount']\n",
    "Male_Adult"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "因为普遍规律是女性和儿童幸存率高，成年男性幸存较低，所以我们把不符合普遍规律的反常组选出来单独处理。把女性和儿童组中幸存率为0的组设置为遇难组，把成年男性组中存活率为1的设置为幸存组，推测处于遇难组的女性和儿童幸存的可能性较低，处于幸存组的成年男性幸存的可能性较高。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Panula', 'Lefebre', 'Lobb', 'Johnston', 'Robins', 'Ilmakangas', 'Turpin', 'Arnold-Franchi', 'Lahtinen', 'Barbara', 'Goodwin', 'Oreskovic', 'Van Impe', 'Strom', 'Rosblom', 'Cacic', 'Attalah', 'Caram', 'Vander Planke', 'Palsson', 'Skoog', 'Danbom', 'Rice', 'Canavan', 'Bourke', 'Jussila', 'Olsson', 'Boulos', 'Zabour', 'Sage', 'Ford'}\n",
      "{'Beane', 'Frauenthal', 'Harder', 'Nakid', 'Bishop', 'Beckwith', 'Bradley', 'Chambers', 'Cardeza', 'Daly', 'Goldenberg', 'Kimball', 'McCoy', 'Jussila', 'Frolicher-Stehli', 'Duff Gordon', 'Greenfield', 'Dick', 'Jonsson', 'Taylor'}\n"
     ]
    }
   ],
   "source": [
    "Female_Child_Group=Female_Child_Group.groupby('Surname')['Survived'].mean()\n",
    "Dead_List=set(Female_Child_Group[Female_Child_Group.apply(lambda x:x==0)].index)\n",
    "print(Dead_List)\n",
    "Male_Adult_List=Male_Adult_Group.groupby('Surname')['Survived'].mean()\n",
    "Survived_List=set(Male_Adult_List[Male_Adult_List.apply(lambda x:x==1)].index)\n",
    "print(Survived_List)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了使处于这两种反常组中的样本能够被正确分类，对测试集中处于反常组中的样本的Age，Title，Sex进行惩罚修改。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train=all_data.loc[all_data['Survived'].notnull()]\n",
    "test=all_data.loc[all_data['Survived'].isnull()]\n",
    "test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Sex'] = 'male'\n",
    "test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Age'] = 60\n",
    "test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Title'] = 'Mr'\n",
    "test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Sex'] = 'female'\n",
    "test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Age'] = 5\n",
    "test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Title'] = 'Miss'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3)特征转换"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "选取特征，转换为数值变量，划分训练集和测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "all_data=pd.concat([train, test])\n",
    "all_data=all_data[['Survived','Pclass','Sex','Age','Fare','Embarked','Title','FamilyLabel','Deck','TicketGroup']]\n",
    "all_data=pd.get_dummies(all_data)\n",
    "train=all_data[all_data['Survived'].notnull()]\n",
    "test=all_data[all_data['Survived'].isnull()].drop('Survived',axis=1)\n",
    "X = train.as_matrix()[:,1:]\n",
    "y = train.as_matrix()[:,0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.建模和优化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1)参数优化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用网格搜索自动化选取最优参数，事实上我用网格搜索得到的最优参数是n_estimators = 28，max_depth = 6。但是参考另一篇Kernel把参数改为n_estimators = 26，max_depth = 6之后交叉验证分数和kaggle评分都有略微提升。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.feature_selection import SelectKBest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'classify__max_depth': 6, 'classify__n_estimators': 42} 0.88109635084\n"
     ]
    }
   ],
   "source": [
    "pipe=Pipeline([('select',SelectKBest(k=20)), \n",
    "               ('classify', RandomForestClassifier(random_state = 10, max_features = 'sqrt'))])\n",
    "\n",
    "param_test = {'classify__n_estimators':list(range(20,50,2)), \n",
    "              'classify__max_depth':list(range(3,60,3))}\n",
    "gsearch = GridSearchCV(estimator = pipe, param_grid = param_test, scoring='roc_auc', cv=10)\n",
    "gsearch.fit(X,y)\n",
    "print(gsearch.best_params_, gsearch.best_score_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2)训练模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.pipeline import make_pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "     steps=[('selectkbest', SelectKBest(k=20, score_func=<function f_classif at 0x000000000C8AE048>)), ('randomforestclassifier', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=6, max_features='sqrt', max_leaf_nodes=None,\n",
       "            min_impurity_decreas...estimators=26, n_jobs=1,\n",
       "            oob_score=False, random_state=10, verbose=0, warm_start=True))])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "select = SelectKBest(k = 20)\n",
    "clf = RandomForestClassifier(random_state = 10, warm_start = True, \n",
    "                                  n_estimators = 26,\n",
    "                                  max_depth = 6, \n",
    "                                  max_features = 'sqrt')\n",
    "pipeline = make_pipeline(select, clf)\n",
    "pipeline.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3)交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import cross_validation, metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CV Score : Mean - 0.8451402 | Std - 0.03276752 \n"
     ]
    }
   ],
   "source": [
    "cv_score = cross_validation.cross_val_score(pipeline, X, y, cv= 10)\n",
    "print(\"CV Score : Mean - %.7g | Std - %.7g \" % (np.mean(cv_score), np.std(cv_score)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5.预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = pipeline.predict(test)\n",
    "submission = pd.DataFrame({\"PassengerId\": PassengerId, \"Survived\": predictions.astype(np.int32)})\n",
    "submission.to_csv(r\"h:\\kaggle\\submission1.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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